Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations608677
Missing cells1435082
Missing cells (%)9.4%
Duplicate rows1703
Duplicate rows (%)0.3%
Total size in memory116.1 MiB
Average record size in memory200.0 B

Variable types

DateTime3
Numeric17
Categorical5

Alerts

Gamma Measured while Drilling has constant value "0.0" Constant
H2S 01 has constant value "0.0" Constant
Dataset has 1703 (0.3%) duplicate rowsDuplicates
Azimuth is highly overall correlated with InclinationHigh correlation
Bit Diameter is highly overall correlated with Hookload and 1 other fieldsHigh correlation
Bit Revolutions per Minute is highly overall correlated with Differential Pressure and 10 other fieldsHigh correlation
Block Position is highly overall correlated with Rig ModeHigh correlation
Depth Hole Total Vertical Depth is highly overall correlated with InclinationHigh correlation
Differential Pressure is highly overall correlated with Bit Revolutions per Minute and 5 other fieldsHigh correlation
Downhole Torque is highly overall correlated with Bit Revolutions per Minute and 6 other fieldsHigh correlation
Flow In is highly overall correlated with Bit Revolutions per Minute and 9 other fieldsHigh correlation
Hookload is highly overall correlated with Bit Diameter and 6 other fieldsHigh correlation
Inclination is highly overall correlated with Azimuth and 1 other fieldsHigh correlation
Mud Temperature is highly overall correlated with Bit Revolutions per Minute and 8 other fieldsHigh correlation
On Bottom is highly overall correlated with Mud Temperature and 4 other fieldsHigh correlation
Pump Pressure is highly overall correlated with Bit Revolutions per Minute and 9 other fieldsHigh correlation
Return Flow is highly overall correlated with Bit Revolutions per Minute and 4 other fieldsHigh correlation
Rig Mode is highly overall correlated with Bit Revolutions per Minute and 4 other fieldsHigh correlation
Top Drive Revolutions per Minute is highly overall correlated with Bit Revolutions per Minute and 5 other fieldsHigh correlation
Top Drive Torque is highly overall correlated with Bit Revolutions per Minute and 8 other fieldsHigh correlation
Total Strokes per Minute is highly overall correlated with Bit Revolutions per Minute and 9 other fieldsHigh correlation
Weight on Bit is highly overall correlated with Bit Diameter and 2 other fieldsHigh correlation
Bit Diameter is highly imbalanced (59.8%) Imbalance
Azimuth has 52962 (8.7%) missing values Missing
Bit Diameter has 74927 (12.3%) missing values Missing
Bit Revolutions per Minute has 83712 (13.8%) missing values Missing
Block Position has 11827 (1.9%) missing values Missing
Depth Hole Total Vertical Depth has 11251 (1.8%) missing values Missing
Differential Pressure has 11814 (1.9%) missing values Missing
Downhole Torque has 103996 (17.1%) missing values Missing
Gamma Measured while Drilling has 524744 (86.2%) missing values Missing
H2S 01 has 311427 (51.2%) missing values Missing
Hookload has 11837 (1.9%) missing values Missing
Inclination has 52957 (8.7%) missing values Missing
Mud Temperature has 11173 (1.8%) missing values Missing
On Bottom has 11813 (1.9%) missing values Missing
Pump Pressure has 11811 (1.9%) missing values Missing
Rate of Penetration (Depth/Hour) has 11813 (1.9%) missing values Missing
Return Flow has 11816 (1.9%) missing values Missing
Rig Mode has 87404 (14.4%) missing values Missing
Top Drive Revolutions per Minute has 11816 (1.9%) missing values Missing
Top Drive Torque has 11816 (1.9%) missing values Missing
Weight on Bit has 11813 (1.9%) missing values Missing
Bit Revolutions per Minute is highly skewed (γ1 = 170.2318309) Skewed
Flow In is highly skewed (γ1 = 32.00149324) Skewed
Hookload is highly skewed (γ1 = 37.1113781) Skewed
Rate of Penetration (Depth/Hour) is highly skewed (γ1 = 200.743463) Skewed
Total Strokes per Minute is highly skewed (γ1 = 32.00098636) Skewed
Weight on Bit is highly skewed (γ1 = -32.40599563) Skewed
Azimuth has 146253 (24.0%) zeros Zeros
Bit Revolutions per Minute has 6811 (1.1%) zeros Zeros
Flow In has 284244 (46.7%) zeros Zeros
Inclination has 146253 (24.0%) zeros Zeros
Pump Pressure has 95931 (15.8%) zeros Zeros
Rate of Penetration (Depth/Hour) has 526321 (86.5%) zeros Zeros
Return Flow has 58396 (9.6%) zeros Zeros
Top Drive Revolutions per Minute has 20125 (3.3%) zeros Zeros
Top Drive Torque has 405112 (66.6%) zeros Zeros
Total Strokes per Minute has 284244 (46.7%) zeros Zeros
Weight on Bit has 7148 (1.2%) zeros Zeros

Reproduction

Analysis started2025-03-17 12:24:17.866090
Analysis finished2025-03-17 12:26:09.081007
Duration1 minute and 51.21 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Date
Date

Distinct102900
Distinct (%)16.9%
Missing1
Missing (%)< 0.1%
Memory size4.6 MiB
Minimum2020-10-25 12:38:00
Maximum2021-01-05 11:01:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-17T07:26:09.191680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:09.333292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct73
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size4.6 MiB
Minimum2020-10-25 00:00:00
Maximum2021-01-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-17T07:26:09.477371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:09.847862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1440
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Memory size4.6 MiB
Minimum2025-03-17 00:00:00
Maximum2025-03-17 23:59:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-17T07:26:09.988384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:10.125339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Azimuth
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct312
Distinct (%)0.1%
Missing52962
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean82.81647
Minimum0
Maximum358.06
Zeros146253
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:10.355397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median103.44
Q3110.04
95-th percentile219.54
Maximum358.06
Range358.06
Interquartile range (IQR)110.04

Descriptive statistics

Standard deviation65.388791
Coefficient of variation (CV)0.78956264
Kurtosis1.6655609
Mean82.81647
Median Absolute Deviation (MAD)7.31
Skewness0.75041627
Sum46022355
Variance4275.694
MonotonicityNot monotonic
2025-03-17T07:26:10.494550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146253
24.0%
103.44 89178
14.7%
110.04 40667
 
6.7%
36.29 29843
 
4.9%
110.53 23438
 
3.9%
110.46 20876
 
3.4%
109.68 17480
 
2.9%
219.54 12879
 
2.1%
108.05 11342
 
1.9%
106.22 10332
 
1.7%
Other values (302) 153427
25.2%
(Missing) 52962
 
8.7%
ValueCountFrequency (%)
0 146253
24.0%
4.38 501
 
0.1%
6.61 243
 
< 0.1%
6.67 1076
 
0.2%
6.76 8
 
< 0.1%
12.11 61
 
< 0.1%
13.79 267
 
< 0.1%
13.88 7
 
< 0.1%
14.78 379
 
0.1%
15.46 8
 
< 0.1%
ValueCountFrequency (%)
358.06 911
0.1%
349 272
 
< 0.1%
337.05 116
 
< 0.1%
335.91 200
 
< 0.1%
335.82 9
 
< 0.1%
334.41 1061
0.2%
332.83 777
0.1%
315.43 1492
0.2%
315.25 251
 
< 0.1%
310.42 574
 
0.1%

Bit Diameter
Categorical

High correlation  Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing74927
Missing (%)12.3%
Memory size31.0 MiB
8.75
355394 
12.0
177485 
12.25
 
832
1.0
 
29
0.0
 
10

Length

Max length5
Median length4
Mean length4.0014857
Min length3

Characters and Unicode

Total characters2135793
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
8.75 355394
58.4%
12.0 177485
29.2%
12.25 832
 
0.1%
1.0 29
 
< 0.1%
0.0 10
 
< 0.1%
(Missing) 74927
 
12.3%

Length

2025-03-17T07:26:10.627046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T07:26:10.712156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
8.75 355394
66.6%
12.0 177485
33.3%
12.25 832
 
0.2%
1.0 29
 
< 0.1%
0.0 10
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 533750
25.0%
5 356226
16.7%
8 355394
16.6%
7 355394
16.6%
2 179149
 
8.4%
1 178346
 
8.4%
0 177534
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2135793
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 533750
25.0%
5 356226
16.7%
8 355394
16.6%
7 355394
16.6%
2 179149
 
8.4%
1 178346
 
8.4%
0 177534
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2135793
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 533750
25.0%
5 356226
16.7%
8 355394
16.6%
7 355394
16.6%
2 179149
 
8.4%
1 178346
 
8.4%
0 177534
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2135793
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 533750
25.0%
5 356226
16.7%
8 355394
16.6%
7 355394
16.6%
2 179149
 
8.4%
1 178346
 
8.4%
0 177534
 
8.3%

Bit Revolutions per Minute
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct220472
Distinct (%)42.0%
Missing83712
Missing (%)13.8%
Infinite0
Infinite (%)0.0%
Mean98.353336
Minimum0
Maximum377404.16
Zeros6811
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:10.853528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0703
Q10.25144
median16.458
Q3145.51305
95-th percentile211.26201
Maximum377404.16
Range377404.16
Interquartile range (IQR)145.26161

Descriptive statistics

Standard deviation1614.4088
Coefficient of variation (CV)16.414378
Kurtosis34983.343
Mean98.353336
Median Absolute Deviation (MAD)16.36758
Skewness170.23183
Sum51632059
Variance2606315.7
MonotonicityNot monotonic
2025-03-17T07:26:11.003539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.21118 13285
 
2.2%
0.19106 13144
 
2.2%
0.17093 12840
 
2.1%
0.23131 12807
 
2.1%
0.25144 12642
 
2.1%
0.27156 11977
 
2.0%
0.1508 11818
 
1.9%
0.29169 11396
 
1.9%
0.13068 10772
 
1.8%
0.31182 10339
 
1.7%
Other values (220462) 403945
66.4%
(Missing) 83712
 
13.8%
ValueCountFrequency (%)
0 6811
1.1%
0.00992 3020
 
0.5%
0.03004 4257
 
0.7%
0.05017 5499
0.9%
0.0703 6943
1.1%
0.09042 8237
1.4%
0.11055 9706
1.6%
0.13068 10772
1.8%
0.1508 11818
1.9%
0.17093 12840
2.1%
ValueCountFrequency (%)
377404.16 1
< 0.1%
377209.84 1
< 0.1%
376819.25 1
< 0.1%
376488.34 1
< 0.1%
376488.22 1
< 0.1%
251907.14 1
< 0.1%
251323.1 1
< 0.1%
250992.33 1
< 0.1%
250992.23 1
< 0.1%
250992.16 1
< 0.1%

Block Position
Real number (ℝ)

High correlation  Missing 

Distinct322815
Distinct (%)54.1%
Missing11827
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean44.968921
Minimum-1511.2948
Maximum116.24767
Zeros269
Zeros (%)< 0.1%
Negative39018
Negative (%)6.4%
Memory size4.6 MiB
2025-03-17T07:26:11.136584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1511.2948
5-th percentile-0.7169
Q113.45396
median43.59582
Q379.778398
95-th percentile103.09833
Maximum116.24767
Range1627.5425
Interquartile range (IQR)66.324438

Descriptive statistics

Standard deviation66.410902
Coefficient of variation (CV)1.4768178
Kurtosis370.06903
Mean44.968921
Median Absolute Deviation (MAD)31.67784
Skewness-16.180356
Sum26839700
Variance4410.4079
MonotonicityNot monotonic
2025-03-17T07:26:11.321627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.984 2548
 
0.4%
21.76608 2178
 
0.4%
49.24219 1646
 
0.3%
27.30117 1619
 
0.3%
27.30118 1592
 
0.3%
21.76609 1579
 
0.3%
20.98396 1497
 
0.2%
23.39897 1310
 
0.2%
71.46714 1305
 
0.2%
-8.80329 1270
 
0.2%
Other values (322805) 580306
95.3%
(Missing) 11827
 
1.9%
ValueCountFrequency (%)
-1511.2948 38
< 0.1%
-1511.2946 6
 
< 0.1%
-1510.4017 1
 
< 0.1%
-1510.1407 11
 
< 0.1%
-1509.8583 7
 
< 0.1%
-1509.7875 1
 
< 0.1%
-1509.7874 55
< 0.1%
-1509.7587 1
 
< 0.1%
-1509.5836 1
 
< 0.1%
-1509.4808 12
 
< 0.1%
ValueCountFrequency (%)
116.24767 1
 
< 0.1%
114.75745 1
 
< 0.1%
114.52909 1
 
< 0.1%
114.4008 1
 
< 0.1%
114.40005 1
 
< 0.1%
114.35046 3
 
< 0.1%
114.35045 9
< 0.1%
114.35044 7
< 0.1%
114.35043 11
< 0.1%
114.30823 1
 
< 0.1%

Depth Hole Total Vertical Depth
Real number (ℝ)

High correlation  Missing 

Distinct101805
Distinct (%)17.0%
Missing11251
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean7085.0323
Minimum2 × 10-5
Maximum735423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:11.450525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2 × 10-5
5-th percentile35.09238
Q15123.895
median7389.89
Q310737.555
95-th percentile10987
Maximum735423
Range735423
Interquartile range (IQR)5613.6605

Descriptive statistics

Standard deviation3416.7931
Coefficient of variation (CV)0.48225512
Kurtosis3455.0715
Mean7085.0323
Median Absolute Deviation (MAD)2358.052
Skewness15.758963
Sum4.2327825 × 109
Variance11674475
MonotonicityNot monotonic
2025-03-17T07:26:11.594451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10987 82712
 
13.6%
1629.0634 33237
 
5.5%
10955 25284
 
4.2%
9747.942 22514
 
3.7%
7389.89 16512
 
2.7%
5123.895 14824
 
2.4%
19.5118 14798
 
2.4%
5504.8726 13086
 
2.1%
7389 12697
 
2.1%
8535.371 12280
 
2.0%
Other values (101795) 349482
57.4%
ValueCountFrequency (%)
2 × 10-52
 
< 0.1%
4 × 10-51
 
< 0.1%
5 × 10-51
 
< 0.1%
6 × 10-52
 
< 0.1%
8 × 10-56
< 0.1%
0.0001 6
< 0.1%
0.00011 8
< 0.1%
0.00012 1
 
< 0.1%
0.00013 3
 
< 0.1%
0.00014 14
< 0.1%
ValueCountFrequency (%)
735423 1
 
< 0.1%
11146.218 8
 
< 0.1%
11146.208 1
 
< 0.1%
11144.107 54
 
< 0.1%
11135.924 1
 
< 0.1%
11114.503 2197
0.4%
11112.5625 1
 
< 0.1%
11108.576 1
 
< 0.1%
11106.996 1
 
< 0.1%
11104.497 1
 
< 0.1%

Differential Pressure
Real number (ℝ)

High correlation  Missing 

Distinct541883
Distinct (%)90.8%
Missing11814
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-681.1211
Minimum-4751.7217
Maximum8598.29
Zeros4
Zeros (%)< 0.1%
Negative393084
Negative (%)64.6%
Memory size4.6 MiB
2025-03-17T07:26:11.749484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-4751.7217
5-th percentile-3270.2416
Q1-1828.7427
median-524.9963
Q341.20937
95-th percentile319.66396
Maximum8598.29
Range13350.012
Interquartile range (IQR)1869.9521

Descriptive statistics

Standard deviation2015.1309
Coefficient of variation (CV)-2.9585501
Kurtosis10.611793
Mean-681.1211
Median Absolute Deviation (MAD)702.99458
Skewness2.5035474
Sum-4.0653598 × 108
Variance4060752.5
MonotonicityNot monotonic
2025-03-17T07:26:11.887840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8598.29 10990
 
1.8%
8591.511 6329
 
1.0%
-4751.127 33
 
< 0.1%
-4751.141 27
 
< 0.1%
-4751.128 26
 
< 0.1%
-4751.104 26
 
< 0.1%
-4751.1133 26
 
< 0.1%
-4751.1313 26
 
< 0.1%
-4751.131 26
 
< 0.1%
-4751.117 25
 
< 0.1%
Other values (541873) 579329
95.2%
(Missing) 11814
 
1.9%
ValueCountFrequency (%)
-4751.7217 1
< 0.1%
-4751.5625 1
< 0.1%
-4751.5103 1
< 0.1%
-4751.5063 1
< 0.1%
-4751.4814 1
< 0.1%
-4751.4688 1
< 0.1%
-4751.4478 1
< 0.1%
-4751.437 1
< 0.1%
-4751.423 1
< 0.1%
-4751.4097 1
< 0.1%
ValueCountFrequency (%)
8598.29 10990
1.8%
8598.289 2
 
< 0.1%
8598.285 1
 
< 0.1%
8598.281 1
 
< 0.1%
8598.28 1
 
< 0.1%
8598.242 1
 
< 0.1%
8598.179 1
 
< 0.1%
8598.17 1
 
< 0.1%
8597.712 1
 
< 0.1%
8597.094 1
 
< 0.1%

Downhole Torque
Real number (ℝ)

High correlation  Missing 

Distinct462309
Distinct (%)91.6%
Missing103996
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean-5727.8502
Minimum-123044.59
Maximum222650.45
Zeros2
Zeros (%)< 0.1%
Negative333063
Negative (%)54.7%
Memory size4.6 MiB
2025-03-17T07:26:12.019442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-123044.59
5-th percentile-56281.266
Q1-22020.047
median-7433.422
Q3578.8529
95-th percentile6498.125
Maximum222650.45
Range345695.04
Interquartile range (IQR)22598.9

Descriptive statistics

Standard deviation47760.968
Coefficient of variation (CV)-8.338376
Kurtosis15.549084
Mean-5727.8502
Median Absolute Deviation (MAD)9798.9845
Skewness3.4589724
Sum-2.8907371 × 109
Variance2.2811101 × 109
MonotonicityNot monotonic
2025-03-17T07:26:12.150083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222650.45 10990
 
1.8%
222474.9 6329
 
1.0%
-123031.05 20
 
< 0.1%
-123029.03 19
 
< 0.1%
-123029.22 19
 
< 0.1%
-123029.58 19
 
< 0.1%
-123031.305 18
 
< 0.1%
-123028.98 18
 
< 0.1%
-123029.55 18
 
< 0.1%
-123029.28 17
 
< 0.1%
Other values (462299) 487214
80.0%
(Missing) 103996
 
17.1%
ValueCountFrequency (%)
-123044.586 1
< 0.1%
-123040.47 1
< 0.1%
-123039.1 1
< 0.1%
-123039.01 1
< 0.1%
-123038.36 1
< 0.1%
-123038.03 1
< 0.1%
-123037.49 1
< 0.1%
-123037.22 1
< 0.1%
-123036.84 1
< 0.1%
-123036.51 1
< 0.1%
ValueCountFrequency (%)
222650.45 10990
1.8%
222650.44 1
 
< 0.1%
222650.42 1
 
< 0.1%
222650.34 1
 
< 0.1%
222650.22 1
 
< 0.1%
222650.17 1
 
< 0.1%
222649.23 1
 
< 0.1%
222647.36 1
 
< 0.1%
222647.2 1
 
< 0.1%
222635.48 1
 
< 0.1%

Flow In
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct41113
Distinct (%)6.8%
Missing1175
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean378.24392
Minimum0
Maximum132667.2
Zeros284244
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:12.280274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53.98838
Q3563.39465
95-th percentile750.19934
Maximum132667.2
Range132667.2
Interquartile range (IQR)563.39465

Descriptive statistics

Standard deviation3538.1815
Coefficient of variation (CV)9.3542323
Kurtosis1080.22
Mean378.24392
Median Absolute Deviation (MAD)53.98838
Skewness32.001493
Sum2.2978394 × 108
Variance12518728
MonotonicityNot monotonic
2025-03-17T07:26:12.424919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284244
46.7%
352.51685 542
 
0.1%
352.02243 471
 
0.1%
347.15353 434
 
0.1%
348.6 428
 
0.1%
348.11652 401
 
0.1%
274.00873 401
 
0.1%
273.70993 386
 
0.1%
236.56174 370
 
0.1%
347.63434 366
 
0.1%
Other values (41103) 319459
52.5%
(Missing) 1175
 
0.2%
ValueCountFrequency (%)
0 284244
46.7%
5.67734 3
 
< 0.1%
7.73926 7
 
< 0.1%
8.6534 1
 
< 0.1%
8.67194 1
 
< 0.1%
10.1781 2
 
< 0.1%
10.59709 7
 
< 0.1%
10.63616 5
 
< 0.1%
11.08671 1
 
< 0.1%
11.0921 4
 
< 0.1%
ValueCountFrequency (%)
132667.2 1
 
< 0.1%
125947.42 1
 
< 0.1%
125932.51 1
 
< 0.1%
125922.13 1
 
< 0.1%
125901.48 4
< 0.1%
125900.83 1
 
< 0.1%
125896.945 1
 
< 0.1%
125896.305 2
< 0.1%
125895.67 2
< 0.1%
125895.03 3
< 0.1%

Gamma Measured while Drilling
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing524744
Missing (%)86.2%
Memory size32.2 MiB
0.0
83933 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters251799
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83933
 
13.8%
(Missing) 524744
86.2%

Length

2025-03-17T07:26:12.541741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T07:26:12.595961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83933
100.0%

Most occurring characters

ValueCountFrequency (%)
0 167866
66.7%
. 83933
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 251799
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 167866
66.7%
. 83933
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 251799
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 167866
66.7%
. 83933
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 251799
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 167866
66.7%
. 83933
33.3%

H2S 01
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing311427
Missing (%)51.2%
Memory size31.4 MiB
0.0
297250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters891750
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 297250
48.8%
(Missing) 311427
51.2%

Length

2025-03-17T07:26:12.668384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T07:26:12.728364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 297250
100.0%

Most occurring characters

ValueCountFrequency (%)
0 594500
66.7%
. 297250
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 891750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 594500
66.7%
. 297250
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 891750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 594500
66.7%
. 297250
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 891750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 594500
66.7%
. 297250
33.3%

Hookload
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct13491
Distinct (%)2.3%
Missing11837
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean124.8516
Minimum0
Maximum15468.208
Zeros4472
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:12.809467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36.7632
Q141.62508
median123.8774
Q3197.84518
95-th percentile255.59076
Maximum15468.208
Range15468.208
Interquartile range (IQR)156.2201

Descriptive statistics

Standard deviation115.09885
Coefficient of variation (CV)0.92188525
Kurtosis3790.4917
Mean124.8516
Median Absolute Deviation (MAD)81.82584
Skewness37.111378
Sum74516429
Variance13247.745
MonotonicityNot monotonic
2025-03-17T07:26:12.945608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4472
 
0.7%
40.55888 1521
 
0.2%
40.81477 1512
 
0.2%
40.68682 1508
 
0.2%
40.77212 1501
 
0.2%
41.07066 1495
 
0.2%
41.11331 1495
 
0.2%
40.64418 1491
 
0.2%
40.60153 1487
 
0.2%
40.98536 1467
 
0.2%
Other values (13481) 578891
95.1%
(Missing) 11837
 
1.9%
ValueCountFrequency (%)
0 4472
0.7%
0.00051 15
 
< 0.1%
0.04316 13
 
< 0.1%
0.0858 14
 
< 0.1%
0.12845 14
 
< 0.1%
0.14593 1
 
< 0.1%
0.1711 10
 
< 0.1%
0.21375 9
 
< 0.1%
0.2564 7
 
< 0.1%
0.29905 8
 
< 0.1%
ValueCountFrequency (%)
15468.208 1
< 0.1%
14651.879 1
< 0.1%
14175.687 1
< 0.1%
14083.649 1
< 0.1%
13747.515 1
< 0.1%
13583.448 1
< 0.1%
12471 2
< 0.1%
12466.998 1
< 0.1%
11522.618 1
< 0.1%
9933.978 2
< 0.1%

Inclination
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct286
Distinct (%)0.1%
Missing52957
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean26.457116
Minimum0
Maximum169.27
Zeros146253
Zeros (%)24.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:13.087698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.93
Q365.4
95-th percentile68.6
Maximum169.27
Range169.27
Interquartile range (IQR)65.4

Descriptive statistics

Standard deviation31.541369
Coefficient of variation (CV)1.1921696
Kurtosis-1.3275795
Mean26.457116
Median Absolute Deviation (MAD)1.93
Skewness0.57094855
Sum14702748
Variance994.85794
MonotonicityNot monotonic
2025-03-17T07:26:13.229414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146253
24.0%
68.6 89168
14.6%
1.93 40680
 
6.7%
1.32 29843
 
4.9%
65.5 23441
 
3.9%
58.13 20527
 
3.4%
1.89 17491
 
2.9%
1.14 16053
 
2.6%
18.16 10335
 
1.7%
61 9874
 
1.6%
Other values (276) 152055
25.0%
(Missing) 52957
 
8.7%
ValueCountFrequency (%)
0 146253
24.0%
0.21 57
 
< 0.1%
0.48 358
 
0.1%
0.53 318
 
0.1%
0.66 580
 
0.1%
0.7 5299
 
0.9%
0.75 878
 
0.1%
0.79 1327
 
0.2%
0.84 1508
 
0.2%
0.88 640
 
0.1%
ValueCountFrequency (%)
169.27 6
 
< 0.1%
134.15 5
 
< 0.1%
125.03 2929
0.5%
99.82 26
 
< 0.1%
91.38 326
 
0.1%
87.69 5
 
< 0.1%
83.17 119
 
< 0.1%
81.87 95
 
< 0.1%
78.95 5
 
< 0.1%
70.4 1375
0.2%

Mud Temperature
Real number (ℝ)

High correlation  Missing 

Distinct3022
Distinct (%)0.5%
Missing11173
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean86.74595
Minimum0
Maximum299.5
Zeros2281
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:13.356926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.1
Q145.7
median80.25
Q3125.5
95-th percentile155.7
Maximum299.5
Range299.5
Interquartile range (IQR)79.8

Descriptive statistics

Standard deviation43.757211
Coefficient of variation (CV)0.50442944
Kurtosis-1.3511559
Mean86.74595
Median Absolute Deviation (MAD)39
Skewness0.20995893
Sum51831052
Variance1914.6935
MonotonicityNot monotonic
2025-03-17T07:26:13.483229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2281
 
0.4%
51.6 1216
 
0.2%
36.1 1125
 
0.2%
42.8 1122
 
0.2%
36.95 1086
 
0.2%
31.45 1064
 
0.2%
43.25 1027
 
0.2%
35.4 1014
 
0.2%
39.95 994
 
0.2%
43.9 993
 
0.2%
Other values (3012) 585582
96.2%
(Missing) 11173
 
1.8%
ValueCountFrequency (%)
0 2281
0.4%
10.25 3
 
< 0.1%
10.4 37
 
< 0.1%
10.45 7
 
< 0.1%
10.7 33
 
< 0.1%
10.85 19
 
< 0.1%
11.1 1
 
< 0.1%
11.15 12
 
< 0.1%
11.3 18
 
< 0.1%
11.55 30
 
< 0.1%
ValueCountFrequency (%)
299.5 1
 
< 0.1%
173.1 2
 
< 0.1%
172.95 9
< 0.1%
172.9 1
 
< 0.1%
172.7 3
 
< 0.1%
172.65 2
 
< 0.1%
172.5 4
< 0.1%
172.25 7
< 0.1%
172.1 2
 
< 0.1%
172.05 2
 
< 0.1%

On Bottom
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing11813
Missing (%)1.9%
Memory size30.2 MiB
0.0
479400 
1.0
117464 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1790592
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 479400
78.8%
1.0 117464
 
19.3%
(Missing) 11813
 
1.9%

Length

2025-03-17T07:26:13.590074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T07:26:13.648942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 479400
80.3%
1.0 117464
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0 1076264
60.1%
. 596864
33.3%
1 117464
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1790592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1076264
60.1%
. 596864
33.3%
1 117464
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1790592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1076264
60.1%
. 596864
33.3%
1 117464
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1790592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1076264
60.1%
. 596864
33.3%
1 117464
 
6.6%

Pump Pressure
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct243439
Distinct (%)40.8%
Missing11811
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1162.5878
Minimum0
Maximum10860.391
Zeros95931
Zeros (%)15.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:13.743308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.35185
median40.267315
Q32179.2949
95-th percentile3731.4418
Maximum10860.391
Range10860.391
Interquartile range (IQR)2173.9431

Descriptive statistics

Standard deviation2080.9374
Coefficient of variation (CV)1.7899184
Kurtosis10.960699
Mean1162.5878
Median Absolute Deviation (MAD)40.267315
Skewness3.0221215
Sum6.9390913 × 108
Variance4330300.3
MonotonicityNot monotonic
2025-03-17T07:26:13.867642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 95931
 
15.8%
10860.391 11015
 
1.8%
10853.611 6343
 
1.0%
6.69065 71
 
< 0.1%
5.67797 68
 
< 0.1%
6.35472 68
 
< 0.1%
5.16795 67
 
< 0.1%
6.98979 67
 
< 0.1%
6.60483 67
 
< 0.1%
7.07561 66
 
< 0.1%
Other values (243429) 483103
79.4%
(Missing) 11811
 
1.9%
ValueCountFrequency (%)
0 95931
15.8%
0.00155 4
 
< 0.1%
0.004 6
 
< 0.1%
0.00645 10
 
< 0.1%
0.00762 1
 
< 0.1%
0.00891 7
 
< 0.1%
0.01136 9
 
< 0.1%
0.01172 1
 
< 0.1%
0.01381 7
 
< 0.1%
0.01626 10
 
< 0.1%
ValueCountFrequency (%)
10860.391 11015
1.8%
10853.611 6343
1.0%
5329.184 1
 
< 0.1%
4898.7163 1
 
< 0.1%
4895.535 1
 
< 0.1%
4747.99 1
 
< 0.1%
4677.556 1
 
< 0.1%
4659.382 1
 
< 0.1%
4651.6455 1
 
< 0.1%
4651.334 1
 
< 0.1%

Rate of Penetration (Depth/Hour)
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct43155
Distinct (%)7.2%
Missing11813
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean11.258759
Minimum0
Maximum147628.9
Zeros526321
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:13.982536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile42.721367
Maximum147628.9
Range147628.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation445.27147
Coefficient of variation (CV)39.548894
Kurtosis54131.09
Mean11.258759
Median Absolute Deviation (MAD)0
Skewness200.74346
Sum6719947.9
Variance198266.68
MonotonicityNot monotonic
2025-03-17T07:26:14.302253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 526321
86.5%
24.35408 101
 
< 0.1%
5.98836 22
 
< 0.1%
43.11266 17
 
< 0.1%
4.49045 15
 
< 0.1%
12.9483 14
 
< 0.1%
12.03083 14
 
< 0.1%
1.55663 13
 
< 0.1%
18.52161 10
 
< 0.1%
11.81958 10
 
< 0.1%
Other values (43145) 70327
 
11.6%
(Missing) 11813
 
1.9%
ValueCountFrequency (%)
0 526321
86.5%
1 × 10-57
 
< 0.1%
3 × 10-51
 
< 0.1%
6 × 10-52
 
< 0.1%
8 × 10-57
 
< 0.1%
0.00014 1
 
< 0.1%
0.00017 2
 
< 0.1%
0.00019 1
 
< 0.1%
0.00021 3
 
< 0.1%
0.00023 1
 
< 0.1%
ValueCountFrequency (%)
147628.9 1
< 0.1%
133344.42 1
< 0.1%
131441.16 1
< 0.1%
97313.26 1
< 0.1%
84171.836 1
< 0.1%
51716.51 1
< 0.1%
48925.246 1
< 0.1%
48589.5 1
< 0.1%
40989.59 1
< 0.1%
35501.062 1
< 0.1%

Return Flow
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct24834
Distinct (%)4.2%
Missing11816
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean30.489147
Minimum0
Maximum100.0496
Zeros58396
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:14.428782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.22641
median31.99345
Q357.50668
95-th percentile75.14337
Maximum100.0496
Range100.0496
Interquartile range (IQR)55.28027

Descriptive statistics

Standard deviation27.651717
Coefficient of variation (CV)0.90693641
Kurtosis-1.6497229
Mean30.489147
Median Absolute Deviation (MAD)28.12429
Skewness0.15383439
Sum18197783
Variance764.61747
MonotonicityNot monotonic
2025-03-17T07:26:14.567784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58396
 
9.6%
2.15897 932
 
0.2%
2.1397 919
 
0.2%
2.1686 887
 
0.1%
2.15415 868
 
0.1%
2.14452 862
 
0.1%
2.16379 852
 
0.1%
2.14933 846
 
0.1%
2.17342 835
 
0.1%
2.13488 811
 
0.1%
Other values (24824) 530653
87.2%
(Missing) 11816
 
1.9%
ValueCountFrequency (%)
0 58396
9.6%
0.00075 116
 
< 0.1%
0.00127 11
 
< 0.1%
0.00557 98
 
< 0.1%
0.00594 11
 
< 0.1%
0.00687 1
 
< 0.1%
0.01039 97
 
< 0.1%
0.0106 21
 
< 0.1%
0.0152 78
 
< 0.1%
0.01526 31
 
< 0.1%
ValueCountFrequency (%)
100.0496 1
 
< 0.1%
100.03996 1
 
< 0.1%
99.99661 3
< 0.1%
99.9677 1
 
< 0.1%
99.95325 1
 
< 0.1%
99.94843 1
 
< 0.1%
99.94362 1
 
< 0.1%
99.93398 1
 
< 0.1%
99.92916 2
< 0.1%
99.91471 1
 
< 0.1%

Rig Mode
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing87404
Missing (%)14.4%
Memory size30.7 MiB
3.0
166518 
2.0
149314 
1.0
103975 
999.0
101466 

Length

Max length5
Median length3
Mean length3.3893008
Min length3

Characters and Unicode

Total characters1766751
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 166518
27.4%
2.0 149314
24.5%
1.0 103975
17.1%
999.0 101466
16.7%
(Missing) 87404
14.4%

Length

2025-03-17T07:26:14.700916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-17T07:26:14.787220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 166518
31.9%
2.0 149314
28.6%
1.0 103975
19.9%
999.0 101466
19.5%

Most occurring characters

ValueCountFrequency (%)
. 521273
29.5%
0 521273
29.5%
9 304398
17.2%
3 166518
 
9.4%
2 149314
 
8.5%
1 103975
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1766751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 521273
29.5%
0 521273
29.5%
9 304398
17.2%
3 166518
 
9.4%
2 149314
 
8.5%
1 103975
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1766751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 521273
29.5%
0 521273
29.5%
9 304398
17.2%
3 166518
 
9.4%
2 149314
 
8.5%
1 103975
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1766751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 521273
29.5%
0 521273
29.5%
9 304398
17.2%
3 166518
 
9.4%
2 149314
 
8.5%
1 103975
 
5.9%

Top Drive Revolutions per Minute
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6519
Distinct (%)1.1%
Missing11816
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean14.726232
Minimum0
Maximum227.71463
Zeros20125
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:14.906708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03004
Q10.18942
median0.31182
Q324.9064
95-th percentile69.00357
Maximum227.71463
Range227.71463
Interquartile range (IQR)24.71698

Descriptive statistics

Standard deviation24.99593
Coefficient of variation (CV)1.6973745
Kurtosis1.9930934
Mean14.726232
Median Absolute Deviation (MAD)0.18114
Skewness1.6593697
Sum8789513.3
Variance624.79653
MonotonicityNot monotonic
2025-03-17T07:26:15.043888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.19106 20280
 
3.3%
0 20125
 
3.3%
0.21118 20053
 
3.3%
0.17093 19852
 
3.3%
0.23131 19783
 
3.3%
0.25144 19457
 
3.2%
0.1508 18859
 
3.1%
0.27156 18715
 
3.1%
0.29169 17564
 
2.9%
0.13068 17305
 
2.8%
Other values (6509) 404868
66.5%
ValueCountFrequency (%)
0 20125
3.3%
0.00824 174
 
< 0.1%
0.00992 5359
 
0.9%
0.02837 189
 
< 0.1%
0.03004 7266
 
1.2%
0.0485 260
 
< 0.1%
0.05017 9209
1.5%
0.06863 411
 
0.1%
0.0703 11436
1.9%
0.08877 750
 
0.1%
ValueCountFrequency (%)
227.71463 1
< 0.1%
209.57623 1
< 0.1%
148.02022 1
< 0.1%
144.1894 1
< 0.1%
144.14914 1
< 0.1%
143.26337 1
< 0.1%
142.25679 1
< 0.1%
140.25139 1
< 0.1%
139.30545 1
< 0.1%
137.28433 1
< 0.1%

Top Drive Torque
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct12339
Distinct (%)2.1%
Missing11816
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean1612.5876
Minimum0
Maximum31021.443
Zeros405112
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:15.173081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32169.3882
95-th percentile9120.69
Maximum31021.443
Range31021.443
Interquartile range (IQR)2169.3882

Descriptive statistics

Standard deviation3171.31
Coefficient of variation (CV)1.9665971
Kurtosis5.4098575
Mean1612.5876
Median Absolute Deviation (MAD)0
Skewness2.3459377
Sum9.6249063 × 108
Variance10057207
MonotonicityNot monotonic
2025-03-17T07:26:15.371816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 405112
66.6%
0.77642 550
 
0.1%
2.80127 426
 
0.1%
4.82612 273
 
< 0.1%
6.85096 189
 
< 0.1%
561.48605 141
 
< 0.1%
565.53795 137
 
< 0.1%
4167.913 132
 
< 0.1%
4169.938 131
 
< 0.1%
559.46011 129
 
< 0.1%
Other values (12329) 189641
31.2%
(Missing) 11816
 
1.9%
ValueCountFrequency (%)
0 405112
66.6%
0.29885 11
 
< 0.1%
0.77642 550
 
0.1%
2.3248 6
 
< 0.1%
2.80127 426
 
0.1%
4.35074 1
 
< 0.1%
4.82612 273
 
< 0.1%
6.37669 1
 
< 0.1%
6.85096 189
 
< 0.1%
8.87581 108
 
< 0.1%
ValueCountFrequency (%)
31021.443 1
< 0.1%
28468.9 1
< 0.1%
26520.207 1
< 0.1%
26198.256 1
< 0.1%
26196.23 1
< 0.1%
26141.56 1
< 0.1%
26080.814 1
< 0.1%
25299.223 1
< 0.1%
24878.055 1
< 0.1%
24491.309 1
< 0.1%

Total Strokes per Minute
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct40160
Distinct (%)6.6%
Missing1175
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean90.483679
Minimum0
Maximum31714.285
Zeros284244
Zeros (%)46.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2025-03-17T07:26:15.639977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12.90878
Q3134.6803
95-th percentile180.18018
Maximum31714.285
Range31714.285
Interquartile range (IQR)134.6803

Descriptive statistics

Standard deviation845.80987
Coefficient of variation (CV)9.3476511
Kurtosis1080.1976
Mean90.483679
Median Absolute Deviation (MAD)12.90878
Skewness32.000986
Sum54969016
Variance715394.34
MonotonicityNot monotonic
2025-03-17T07:26:15.780864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 284244
46.7%
84.26966 542
 
0.1%
84.15147 479
 
0.1%
82.98755 434
 
0.1%
83.33334 424
 
0.1%
83.21775 403
 
0.1%
65.50218 401
 
0.1%
65.43076 386
 
0.1%
56.55042 370
 
0.1%
83.10249 366
 
0.1%
Other values (40150) 319453
52.5%
(Missing) 1175
 
0.2%
ValueCountFrequency (%)
0 284244
46.7%
1.85008 7
 
< 0.1%
1.95906 3
 
< 0.1%
2.06861 1
 
< 0.1%
2.07304 1
 
< 0.1%
2.43309 2
 
< 0.1%
2.53325 7
 
< 0.1%
2.54259 5
 
< 0.1%
2.65029 1
 
< 0.1%
2.65158 4
 
< 0.1%
ValueCountFrequency (%)
31714.285 1
 
< 0.1%
30107.914 1
 
< 0.1%
30104.348 1
 
< 0.1%
30101.867 1
 
< 0.1%
30096.93 4
< 0.1%
30096.773 1
 
< 0.1%
30095.846 1
 
< 0.1%
30095.693 2
< 0.1%
30095.541 2
< 0.1%
30095.389 3
< 0.1%

Weight on Bit
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct571023
Distinct (%)95.7%
Missing11813
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean72.997994
Minimum-15599.576
Maximum837.66199
Zeros7148
Zeros (%)1.2%
Negative105196
Negative (%)17.3%
Memory size4.6 MiB
2025-03-17T07:26:15.956916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15599.576
5-th percentile-42.463463
Q12.177595
median46.95764
Q3172.12066
95-th percentile202.53039
Maximum837.66199
Range16437.238
Interquartile range (IQR)169.94307

Descriptive statistics

Standard deviation115.73301
Coefficient of variation (CV)1.5854273
Kurtosis3177.3859
Mean72.997994
Median Absolute Deviation (MAD)51.617135
Skewness-32.405996
Sum43569874
Variance13394.129
MonotonicityNot monotonic
2025-03-17T07:26:16.148298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7148
 
1.2%
69.53371 6
 
< 0.1%
0.03833 4
 
< 0.1%
-1.17484 4
 
< 0.1%
-0.31315 4
 
< 0.1%
178.30861 4
 
< 0.1%
0.31701 4
 
< 0.1%
180.24648 4
 
< 0.1%
0.39915 4
 
< 0.1%
-0.20755 4
 
< 0.1%
Other values (571013) 589678
96.9%
(Missing) 11813
 
1.9%
ValueCountFrequency (%)
-15599.576 1
< 0.1%
-14182.979 1
< 0.1%
-14047.853 1
< 0.1%
-13166.369 1
< 0.1%
-12421.052 1
< 0.1%
-12376.617 1
< 0.1%
-12294.487 1
< 0.1%
-11126.89 1
< 0.1%
-10347.741 1
< 0.1%
-9895.907 1
< 0.1%
ValueCountFrequency (%)
837.66199 1
< 0.1%
837.65849 1
< 0.1%
837.65805 1
< 0.1%
837.65788 1
< 0.1%
837.65009 1
< 0.1%
837.5592 1
< 0.1%
836.46167 1
< 0.1%
823.06122 1
< 0.1%
728.4349 1
< 0.1%
716.9261 1
< 0.1%

Interactions

2025-03-17T07:25:58.891651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:47.133892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:50.458341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:53.908333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:57.403308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:01.493733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:04.972702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:08.039690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:11.923777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:16.713342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:20.802405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:24.765956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:31.274067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:37.284929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:45.088338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:49.786742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:53.971413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:59.182755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:47.320526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:50.649503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:54.108072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:57.598300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:01.677226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:05.152667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:08.224650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:12.153366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:16.939563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:21.018719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:24.976133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:31.622289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:37.547661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:45.302931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:49.968975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:54.173829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:59.528186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:47.526664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:50.853008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:54.316888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:57.814407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:02.028029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:05.331799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:08.421664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:12.394141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:17.191453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:21.244350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:25.205450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:32.002090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:37.814166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:45.596354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:50.194587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:54.611486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:00.092615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:47.734710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:51.054676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:54.526336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:58.022565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:02.229333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:05.516474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:08.620867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:12.638556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:17.496274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:21.477931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:25.471981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:32.448236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:38.241544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:45.819560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:50.414090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:54.829202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:00.364509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:47.939945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:51.258272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:54.741353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:58.227895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:02.423884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:05.699309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:08.839343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:12.921777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:17.738746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:21.716658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:25.998273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:33.068693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:39.495495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:46.229687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:50.642397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:55.053374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:00.587258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:48.122824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:51.477300image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:54.933443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:58.419171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:02.598515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:05.877951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:09.132630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:13.149103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:17.968776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:21.928357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:26.608465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:33.490110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:40.078258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:46.423745image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:18.415972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:22.391808image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:27.825572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:34.138923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:40.933740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:24:52.141019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:55.555794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:03.174505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:06.398113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:09.807664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:13.861041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:24:59.284149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:03.372725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:06.575240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:10.017957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:14.543741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:18.913825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:14.791248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:19.213980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:23.204481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:28.997815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:35.268109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:43.082324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:47.660124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:56.357777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:02.039188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:49.299067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:52.726354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:56.181286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:59.816795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:03.788845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:06.935784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:10.432805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:15.042631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:19.444735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:23.437118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:29.364530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:43.476310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:04.000674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:07.110159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:10.635533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:15.318978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:19.689430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:23.671028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:29.708017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:35.868247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:43.814834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:48.566912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:52.882316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:56.968619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:02.555572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:49.690430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:53.119829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:56.593016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:00.343311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:04.195942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:07.284756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:10.844245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:15.575191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:57.180915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:02.769746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:24:53.322630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:56.794940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:00.815994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:04.388887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:07.468057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:11.038574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:15.846089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:20.120173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:24.100467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:30.273106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:36.457972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:44.356628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:53.310267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:57.403417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:02.989519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:50.077580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:53.515792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:57.004520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:20.362209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:24.329867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:30.567924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:36.744655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:44.628898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-03-17T07:25:57.804461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:26:03.193376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:50.275409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:53.712011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:24:57.207567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:01.268651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:04.800389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:07.854376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:11.689635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:16.452047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:20.581453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:24.555373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:30.841897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:37.019778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:44.872711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:49.604848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:53.759678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-17T07:25:58.139355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-17T07:26:16.484968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AzimuthBit DiameterBit Revolutions per MinuteBlock PositionDepth Hole Total Vertical DepthDifferential PressureDownhole TorqueFlow InHookloadInclinationMud TemperatureOn BottomPump PressureRate of Penetration (Depth/Hour)Return FlowRig ModeTop Drive Revolutions per MinuteTop Drive TorqueTotal Strokes per MinuteWeight on Bit
Azimuth1.0000.3370.1570.2370.0250.0690.0580.2640.1870.5210.3750.3760.4010.1710.2340.2600.1560.2500.2630.136
Bit Diameter0.3371.0000.0060.0550.0000.1560.4920.0281.0000.3080.1650.2830.1940.0040.2450.2950.1410.1990.0281.000
Bit Revolutions per Minute0.1570.0061.000-0.060-0.0540.5790.5700.9360.5240.0640.7140.0000.7600.4400.6641.0000.7470.7630.936-0.424
Block Position0.2370.055-0.0601.0000.105-0.056-0.045-0.0180.0270.2580.1110.0180.1040.0040.0141.000-0.0010.024-0.0180.161
Depth Hole Total Vertical Depth0.0250.000-0.0540.1051.000-0.212-0.127-0.0080.2480.538-0.0730.0000.153-0.152-0.0380.001-0.130-0.074-0.0090.055
Differential Pressure0.0690.1560.579-0.056-0.2121.0000.9830.5870.2390.0250.4710.4750.6110.4430.3870.3130.4890.5890.587-0.360
Downhole Torque0.0580.4920.570-0.045-0.1270.9831.0000.6030.2870.0730.5090.3100.7150.4140.4020.2290.4880.5760.603-0.338
Flow In0.2640.0280.936-0.018-0.0080.5870.6031.0000.5030.1280.7030.0370.7380.4590.6120.0260.6400.7461.000-0.387
Hookload0.1871.0000.5240.0270.2480.2390.2870.5031.0000.1870.4980.0050.5050.1580.4321.0000.3720.4540.503-0.624
Inclination0.5210.3080.0640.2580.5380.0250.0730.1280.1871.0000.1690.1870.3270.0100.0980.1390.0180.1030.1280.098
Mud Temperature0.3750.1650.7140.111-0.0730.4710.5090.7030.4980.1691.0000.6830.6940.4100.5530.4270.5460.6650.702-0.245
On Bottom0.3760.2830.0000.0180.0000.4750.3100.0370.0050.1870.6831.0000.6450.0000.4800.9630.6380.6160.0370.006
Pump Pressure0.4010.1940.7600.1040.1530.6110.7150.7380.5050.3270.6940.6451.0000.4060.5920.3960.4910.6360.737-0.196
Rate of Penetration (Depth/Hour)0.1710.0040.4400.004-0.1520.4430.4140.4590.1580.0100.4100.0000.4061.0000.4110.0030.4030.4870.459-0.067
Return Flow0.2340.2450.6640.014-0.0380.3870.4020.6120.4320.0980.5530.4800.5920.4111.0000.3220.4330.4930.611-0.195
Rig Mode0.2600.2951.0001.0000.0010.3130.2290.0261.0000.1390.4270.9630.3960.0030.3221.0000.3620.3530.0261.000
Top Drive Revolutions per Minute0.1560.1410.747-0.001-0.1300.4890.4880.6400.3720.0180.5460.6380.4910.4030.4330.3621.0000.7900.640-0.275
Top Drive Torque0.2500.1990.7630.024-0.0740.5890.5760.7460.4540.1030.6650.6160.6360.4870.4930.3530.7901.0000.746-0.309
Total Strokes per Minute0.2630.0280.936-0.018-0.0090.5870.6031.0000.5030.1280.7020.0370.7370.4590.6110.0260.6400.7461.000-0.387
Weight on Bit0.1361.000-0.4240.1610.055-0.360-0.338-0.387-0.6240.098-0.2450.006-0.196-0.067-0.1951.000-0.275-0.309-0.3871.000

Missing values

2025-03-17T07:26:03.413733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-17T07:26:04.350318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-17T07:26:07.483707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DateDate (Date Only)Date (Time Only)AzimuthBit DiameterBit Revolutions per MinuteBlock PositionDepth Hole Total Vertical DepthDifferential PressureDownhole TorqueFlow InGamma Measured while DrillingH2S 01HookloadInclinationMud TemperatureOn BottomPump PressureRate of Penetration (Depth/Hour)Return FlowRig ModeTop Drive Revolutions per MinuteTop Drive TorqueTotal Strokes per MinuteWeight on Bit
0NaTNaTNaTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
12020-10-25 12:38:002020-10-252025-03-17 12:38:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
22020-10-25 12:38:002020-10-252025-03-17 12:38:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
32020-10-25 12:38:002020-10-252025-03-17 12:38:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
42020-10-25 12:38:002020-10-252025-03-17 12:38:00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
52020-10-25 12:39:002020-10-252025-03-17 12:39:000.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
62020-10-25 12:39:002020-10-252025-03-17 12:39:000.0NaNNaNNaNNaNNaNNaNNaN0.0NaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaN
72020-10-25 12:39:002020-10-252025-03-17 12:39:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaN0.0NaN
82020-10-25 12:39:002020-10-252025-03-17 12:39:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaN0.0NaN
92020-10-25 12:39:002020-10-252025-03-17 12:39:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaN0.0NaN
DateDate (Date Only)Date (Time Only)AzimuthBit DiameterBit Revolutions per MinuteBlock PositionDepth Hole Total Vertical DepthDifferential PressureDownhole TorqueFlow InGamma Measured while DrillingH2S 01HookloadInclinationMud TemperatureOn BottomPump PressureRate of Penetration (Depth/Hour)Return FlowRig ModeTop Drive Revolutions per MinuteTop Drive TorqueTotal Strokes per MinuteWeight on Bit
6086672021-01-05 10:59:002021-01-052025-03-17 10:59:000.08.75147.7900134.6175010987.0352.598453655.4540326.56180NaN0.0148.245400.080.10.02099.26950.024.540853.068.762053270.9055112.359552.40370
6086682021-01-05 10:59:002021-01-052025-03-17 10:59:000.08.75148.1072434.6175010987.0353.107673660.7334327.78946NaN0.0148.629240.080.10.02099.35550.024.550493.068.782183046.1472112.781952.36366
6086692021-01-05 10:59:002021-01-052025-03-17 10:59:000.08.75148.1194234.6174910987.0352.551243653.8040326.56180NaN0.0148.288060.080.10.02108.23170.024.617933.068.802313331.6510112.359552.35216
6086702021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75148.0992934.6175010987.0352.021943649.4773327.17447NaN0.0148.543950.080.10.02110.30620.024.593853.068.923063102.8430112.570362.33941
6086712021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75147.9584034.6174910987.0353.790703667.8147327.17447NaN0.0148.330700.080.10.02109.25420.024.579393.068.782183206.1104112.570362.34043
6086722021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75147.9382834.6175010987.0355.495273683.7420327.17447NaN0.0148.074810.080.10.02114.80320.024.516773.068.862692993.5012112.570362.33120
6086732021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75148.2481234.6174910987.0356.439103693.8347326.56180NaN0.0148.416000.080.10.02109.24930.024.540853.068.721803023.8740112.359552.33572
6086742021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75148.0590434.6174910987.0355.766853688.3015327.17447NaN0.0148.671890.080.10.02110.47050.024.560123.068.882813222.3090112.570362.33487
6086752021-01-05 11:00:002021-01-052025-03-17 11:00:000.08.75148.1877434.6174910987.0356.167203692.4521327.78946NaN0.0148.117460.080.10.02107.63350.024.540853.068.862693169.6630112.781952.35398
6086762021-01-05 11:01:002021-01-052025-03-17 11:01:000.08.75147.8101334.6174910987.0355.956853690.2715326.56180NaN0.0148.373350.080.10.02111.89010.024.569763.068.782182959.0789112.359552.35820

Duplicate rows

Most frequently occurring

DateDate (Date Only)Date (Time Only)AzimuthBit DiameterBit Revolutions per MinuteBlock PositionDepth Hole Total Vertical DepthDifferential PressureDownhole TorqueFlow InGamma Measured while DrillingH2S 01HookloadInclinationMud TemperatureOn BottomPump PressureRate of Penetration (Depth/Hour)Return FlowRig ModeTop Drive Revolutions per MinuteTop Drive TorqueTotal Strokes per MinuteWeight on Bit# duplicates
32020-10-25 19:00:002020-10-252025-03-17 19:00:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
42020-10-25 19:01:002020-10-252025-03-17 19:01:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
52020-10-25 19:02:002020-10-252025-03-17 19:02:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
62020-10-25 19:03:002020-10-252025-03-17 19:03:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
72020-10-25 19:04:002020-10-252025-03-17 19:04:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
82020-10-25 19:05:002020-10-252025-03-17 19:05:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
92020-10-25 19:06:002020-10-252025-03-17 19:06:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
102020-10-25 19:07:002020-10-252025-03-17 19:07:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
112020-10-25 19:08:002020-10-252025-03-17 19:08:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6
122020-10-25 19:09:002020-10-252025-03-17 19:09:000.0NaNNaNNaNNaNNaNNaN0.00.0NaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaN0.0NaN6